A Comparative Study of Object Tracking using CNN and SDAE

Object tracking which refers to automatic estimation of the trajectory is a challenging problem. To track the object robustly and efficiently, we explored an autonomous object tracking methodological framework that adopts the deep learning architectures, specifically the convolutional neural network...

Full description

Saved in:
Bibliographic Details
Published in:2018 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 6
Main Authors: Yang, Wei, Wang, Wei, Gao, Yang, Jin, Zhanpeng
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2018
Subjects:
Convolution
convolutional neural network
Feature extraction
Machine learning
Neural networks
Object tracking
particle filter
stacked denoising autoencoder
Target tracking
Trajectory
ISSN:2161-4407
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Object tracking which refers to automatic estimation of the trajectory is a challenging problem. To track the object robustly and efficiently, we explored an autonomous object tracking methodological framework that adopts the deep learning architectures, specifically the convolutional neural network (CNN) and the stacked denoising autoencoder (SDAE), as opposed to the most frequently used tracking algorithms that only learn the appearance of the tracked object. Moreover, we conduct a comparative study of both approaches in terms of tracking accuracy and efficiency. The results show that the features learned by both CNN and SDAE are very supportive in object tracking problem and the detailed comparisons are demonstrated in this work.
ISSN:2161-4407
DOI:10.1109/IJCNN.2018.8489742